Technical Reports - Query Results

This thesis is concerned with efficient methods for achieving
noise-tolerance in Machine Learning algorithms that are capable of
using relational background knowledge. While classical algorithms are
restricted to learn propositional concepts in the form of decision
trees or decision lists, relational learning algorithms are able to
include into the learning process not only knowledge about data
attributes and values, but also about relations between the
attributes. As these algorithms use a more powerful representation
language --- they learn PROLOG programs for classification --- they
are part of the recent field of Inductive Logic Programming, a new
research area at the intersection of Machine Learning and Logic
Programming.
In this work we first review several known methods for achieving
noise-tolerance and put them into a unified framework and then
introduce three new and improved algorithms. The two basic approaches
to pruning are either to try to recognize noise in the data during
the learning process (pre-pruning) or to first learn a theory from the data as
they are and subsequently try to detect and correct the resulting mistakes
in this theory (post-pruning). Both approaches having their advantages, the
major part of this thesis is devoted to trying to combine and integrate them
into new powerful algorithms. A series of experiments with artificial
and natural data sets demonstrates the usefulness of these approaches.